Book Image

R Deep Learning Essentials. - Second Edition

By : Mark Hodnett, Joshua F. Wiley
Book Image

R Deep Learning Essentials. - Second Edition

By: Mark Hodnett, Joshua F. Wiley

Overview of this book

Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.
Table of Contents (13 chapters)

Natural Language Processing Using Deep Learning

This chapter will demonstrate how to use deep learning for natural language processing (NLP). NLP is the processing of human language text. NLP is a broad term for a number of different tasks involving text data, which include (but are not limited to) the following:

  • Document classification: Classifying documents into different categories based on their subject
  • Named entity recognition: Extracting key information from documents, for example, people, organizations, and locations
  • Sentiment analysis: Classifying comments, tweets, or reviews as positive or negative sentiment
  • Language translation: Translating text data from one language to another
  • Part of speech tagging: Assigning the type to each word in a document, which is usually used in conjunction with another task

In this chapter, we will look at document classification, which...